as.tmCorpus |
Create textmining Corpus |
filter_documents |
Function to filter tagged text |
getDoc |
Function to access documents for textmining objects |
getMeta |
Function to access meta data for textmining objects |
make_tabled |
Function to create tmWordCountsTable object from tmParsed |
mallet_prepare |
Helper function to use mallet topic modelling with tmCorpus |
ngram |
Function to create ngram docs |
parse |
Function to parse tmCorpus. As an outpus we have tmParsed object. |
predict |
predict for 'tmTopicModel' object |
predict.jobjRef |
predict for 'tmTopicModel' object |
predict.LDA |
predict for 'tmTopicModel' object |
predict.tmTopicModel |
predict for 'tmTopicModel' object |
setDoc |
Function to change documents for textmining objects |
setMeta |
Function to access meta data for textmining objects |
tabler |
Helper function for tabelarising documents |
terms |
Function to return the most frequent terms of tmTopicModels |
terms.jobjRef |
Function to return the most frequent terms of tmTopicModels |
terms.tmTopicModel |
Function to return the most frequent terms of tmTopicModels |
tmCorpus |
Function to create tmCorpus |
tmMetaData |
Function to create tmMetaData |
tmParsed |
Function to create tmParsed |
tmTaggedCorpus |
Function to create tmTaggedCorpus |
tmTextDocument |
Function to create single tmTextDocument with meta data. The object can store any from of documents: raw (string), parsed or table of words counts. |
tmWordCountsTable |
Function to create tmWordCountsTable |
topic_network |
Function to plot topic network |
topic_table |
Function to calculate topics and words arrays from the mallet model. |
topic_wordcloud |
Simple wordcloud visualization of the topics. |
train |
train for 'tmCorpus' object |
train.DocumentTermMatrix |
train for 'tmCorpus' object |
train.tmCorpus |
train for 'tmCorpus' object |